The integration of generative AI into developer forums like Stack Overflow presents an opportunity to enhance problem-solving by allowing users to post screenshots of code or Integrated Development Environments (IDEs) instead of traditional text-based queries. This study evaluates the effectiveness of various large language models (LLMs)—specifically LLAMA, GEMINI, and GPT-4o in interpreting such visual inputs. We employ prompt engineering techniques, including in-context learning, chain-of-thought prompting, and few-shot learning, to assess each model’s responsiveness and accuracy. Our findings show that while GPT-4 shows promising capabilities, achieving over 60% similarity to baseline questions for 51.75% of the tested images, challenges remain in obtaining consistent and accurate interpretations for more complex images. This research advances our understanding of the feasibility of using generative AI for image-centric problem-solving in developer communities, highlighting both the potential benefits and current limitations of this approach while envisioning a future where visual-based debugging copilot tools become a reality.
Navid Bin Hasan Bangladesh University of Engineering and Technology, Md. Ashraful Islam Bangladesh University of Engineering and Technology, Junaed Younus Khan Bangladesh University of Engineering and Technology, Sanjida Senjik Bangladesh University of Engineering and Technology, Anindya Iqbal Bangladesh University of Engineering and Technology Dhaka, Bangladesh
